Route Coding Tasks to 4 AI Agents With Hermes Bridge

Direct Answer Block

Hermes Code Bridge Multi-Agent Router means using Hermes Agent v0.13 as a central routing layer that assigns coding tasks to Claude Code, OpenAI Codex CLI, Gemini CLI, or OpenCode based on language, complexity, and historical performance. The router maintains a skill registry that tracks which agent performed best on which task type and adjusts routing weights accordingly. Teams save 20-30 hours per week.

The Real Problem

30-60 minutes per developer per day. That is what manual tool selection costs a team using multiple AI coding agents. The senior engineer picks Claude Code for TypeScript, then switches to Codex for the Python data pipeline, then remembers Gemini CLI is better for the GCP deployment config. Each switch requires loading project context, authenticating, and mentally reorienting.

[ STAT ] AI tools could add $2.6-$4.4 trillion annually to the global economy, but ad-hoc adoption prevents most organizations from realizing it. — McKinsey, 2024

For a team of five senior developers at $100/hour, that daily tool-selection tax adds up to $62,500-125,000 per year. The cost is invisible because nobody writes it down. It is just the ambient friction of having multiple powerful tools with no coordination layer between them.

What This Workflow Actually Does

This workflow treats your fleet of AI coding agents as one unified resource pool. You describe the task once. Hermes decides which agent should handle it.

[TOOL: Hermes Agent v0.13] The central router. It reads your project's package.json, CLAUDE.md, and AGENTS.md to determine language and conventions. It evaluates task complexity by counting file references and keywords like refactor or migration. Output: routing recommendation with confidence score.

[TOOL: Claude Code] Primary agent for TypeScript, full-stack, and complex multi-file refactors. Receives tasks with the highest complexity scores.

[TOOL: OpenAI Codex CLI] Primary agent for Python, data pipelines, and test generation. Receives data-intensive and ML-related tasks.

[TOOL: Gemini CLI] Primary agent for Google Cloud operations, API integrations, and infrastructure-as-code. Receives GCP-specific tasks.

[TOOL: OpenCode] Primary agent for experimental work, model comparisons, and R&D tasks where model flexibility matters.

The agentic reasoning step: Hermes does not round-robin. It reads your project conventions, evaluates the task against its routing table, and picks the best fit. If Claude Code historically scored well on TypeScript refactors, Hermes weights it higher for future TypeScript tasks. This is learned routing, not static configuration.

Who This Is Built For

Technical team leads managing 4-8 developers who each prefer different AI coding tools. You need to standardize without forcing a single tool on everyone. This workflow lets developers keep their preferred tool while the router ensures optimal task assignment.

Freelance platform engineers maintaining codebases across Python, TypeScript, Go, and Rust. You need the best model for each language without maintaining four separate tool configurations per project.

DevOps engineers managing infrastructure-as-code alongside application code. You want Gemini CLI for GCP operations and Claude Code for app logic, routed through one interface.

How It Runs: Step By Step

Task Intake: Hermes receives a task via Telegram, CLI, or webhook. Input: natural language description. Output: structured task object with language, scope, and urgency.
Codebase Analysis: Hermes reads your project's instruction files — CLAUDE.md, AGENTS.md — and package.json to determine primary language, framework, and conventions. Output: routing recommendation with confidence score.
Agent Assignment: Hermes consults its routing table — a configurable YAML map that assigns preferred agents per language and task type. Python+FastAPI goes to Codex. Next.js+TypeScript goes to Claude Code. Output: selected agent ID.
MCP Bridge Execution: Hermes opens an MCP session with the selected agent. The agent receives the task with project context and workspace files. Output: execution logs streamed in real time.
Parallel Dispatch: If the task has independent sub-tasks, Hermes splits them. Claude Code handles API routes while Codex handles data migrations. Output: merged result with per-agent attribution.
Output Collection: Each agent returns diffs, test outputs, and commit hashes through the MCP bridge. Hermes validates all required files were changed and tests pass. Output: consolidated summary.
Human Review: The developer reviews and sends /approve, /reject, or /revise. Rejected tasks return to step 3 with a different agent. Output: final approval.
Routing Update: Manual overrides are recorded and future routing weights adjusted. Output: an evolving routing table that gets smarter with each task.

Setup and Tools

Setup time: 120 minutes. Most of that is configuring the routing table and testing each agent's MCP bridge individually.

Hermes Agent v0.13 → Central router (routing table, MCP client, skill registry) Claude Code → TypeScript/full-stack specialist (Anthropic API) OpenAI Codex CLI → Python/data specialist (ChatGPT subscription or API) Gemini CLI → GCP/infra specialist (Google Cloud project required) OpenCode → Experimental/model-agnostic (75+ provider support)

Gotcha: Each agent reads a different instruction file format. Claude Code uses CLAUDE.md. Codex uses AGENTS.md. OpenCode supports both but reads them in a different priority order. You must maintain both files in your repo root with consistent conventions, or agents will produce style-inconsistent code.

The Numbers

▸ Tool selection overhead 30-60 min/day per dev → under 2 min/day with automatic routing ▸ Parallel task execution 1 task at a time → 3-4 in parallel via multi-agent dispatch ▸ Context file duplication 15-20 min per project → one set of instruction files ▸ Multi-language features (Python+TS) 8-12 hours → 3-5 hours with parallel dispatch ▸ Cost for team of 5 at $100/hr $4,000/week lost → $200/week in API costs

Measurable in week 1: tool selection time. Time how long it takes your team to pick and configure the right tool for a task. Compare to the same task routed through Hermes.

What It Cannot Do

It cannot handle real-time collaborative editing. Agents work on isolated copies of files and Hermes merges with git. Two agents editing the same file will cause merge conflicts.
It cannot normalize output formats across agents. Codex returns unified diffs. Claude Code returns git-formatted patches. Gemini CLI returns inline edits. Your review step must handle multiple formats.
It cannot auto-discover new tools. Adding a fifth agent requires a config entry in the routing table and an MCP server definition. There is no plugin marketplace.

Start in 10 Minutes

(5 min) Install Hermes Agent: curl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.sh | bash. Run hermes setup.
(10 min) Install the two agents you use most: npm install -g @anthropic-ai/claude-code and npm install -g @openai/codex. Authenticate both.
(20 min) Configure the Hermes routing table in config.yaml with your two agents. Add an agent_routing section mapping languages to preferred agents.
(30 min) Test with a multi-file task: Refactor the auth module to use JWT. Watch Hermes pick the agent and route it. If it picks wrong, manually correct it and Hermes adjusts for next time.

FAQ

Q: Can I add a custom agent to the Hermes router? A: Yes. Any tool that exposes an MCP server can be added as a routing target. Add an entry to the mcp_servers section in config.yaml and a corresponding route in the routing table.

Q: Does the router work with local models? A: Yes, if the local model exposes an OpenAI-compatible endpoint. OpenCode supports 75+ providers including Ollama and vLLM for local models.

Q: What happens if the preferred agent is offline? A: Hermes checks agent availability at MCP connection time. If the preferred agent does not respond within 30 seconds (configurable), it falls back to the next-best agent in the routing table.

Q: How does Hermes learn which agent performs best? A: It tracks task completion time, human approval rate, and test pass rate per agent per task type. After 10+ tasks of a given type, the routing weights reflect actual performance data.

Q: Can I route tasks based on cost? A: Yes. The routing table supports a max_cost_per_task parameter. If an agent exceeds it, Hermes routes future similar tasks to a cheaper alternative.

(Source: McKinsey, 2024) (Source: Hermes Atlas, 2026) (Source: Tembo, 2026) (Source: FutureAGI, 2026)

The routing decision is not a one-time choice. Hermes re-evaluates mid-task if the initial agent encounters difficulty. For example, if Codex starts a Python task but hits a type-annotation edge case, Hermes can pause, capture the context, and hand off to Claude Code for completion. This dynamic reassignment prevents the 15-20 minute debugging cycle that happens when a developer realizes they chose the wrong tool mid-task. The MCP bridge supports this handoff transparently because both agents share the same project context via Hermes's session state.